Non-destructive Sensor Research Articles

Sensors and imaging techniques for the assessment of the delay of wheat senescence induced by fungicides.

Near-range and remote sensing techniques are excellent alternatives to destructive methods for measuring beneficial effects of fungicides on plant physiology. Different noninvasive sensors and imaging techniques have been used and compared to measure the effects of three fungicidal compounds (bixafen, fluoxastrobin and prothioconazole) on wheat (Triticum aestivum L.) physiology under disease-free conditions in the greenhouse. Depending on the fungicidal treatment, changes in green leaf area and yield parameters were observed. Chlorophyll fluorescence of leaves was useful for measuring differences in the effective quantum yield of PSII. Reflectance measurements of wheat leaves were highly sensitive to changes in plant vitality. The spectral vegetation indices were useful for determining the differences among treatments in terms of leaf senescence, pigments and water content. The analysis of ear and leaf surface temperature was reliable for detecting effects of fungicides on plant senescence. Using nondestructive sensors, it was possible to assess a delay in senescence of wheat due to fungicide application. Furthermore, it was deduced that sensors and imaging methods are useful tools to estimate the effects of fungicides on wheat physiology. Physiological parameters measured by the sensors were actually more sensitive than yield parameters to assess the effect caused by fungicide application on wheat physiology.

Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt.

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

Hyperspectral Imaging—A New Era of Applications in Non-Destructive Sensing of Meat Quality

Introduction T he high labour costs, inconsistency, subjectivity and variability associated with human evaluation of meat quality justify the need for objective measurement systems. A hyperspectral imaging system has been developed recently as a rapid and effective tool for meat quality assessment, allowing the determination of several quality parameters simultaneously. This technology combines the advantages of both spectroscopy and imaging techniques in one system to provide both spectral and spatial information for better and deep inspection of meat products. Coupled with robust multivariate analyses, the system has been used successfully for differentiation between meat cuts, distinguishing between different quality grades and discrimination between different species. The system has also been utilised for accurate prediction of water holding capacity, colour, pH, drip loss and tenderness, the most important quality traits in beef, pork and lamb. Moreover, the developed hyperspectral imaging system has been exploited not only for predicting major chemical constituents such as moisture, fat and protein in different meat species with reasonable accuracy but also for building chemical images to show the distribution maps of these constituents in a direct and easy way, which facilitates meat grading and labeling. The great potential of this technology elucidated from these research endeavours promotes this technology as a good candidate and a better choice for in-line or at-line assessment of meat quality non-destructively in the modern meat industry. The question that needs to be answered now is “how all of these tasks could be achieved using a clear-cut methodology”? The answer to this question is also straightforward “you need to have four things in your hands: (i) high-quality spectral images inside which the spectral fingerprints (X-matrix) of the meat samples being analysed reside, (ii) the reference values (Y-matrix) of all traits that you want to predict, (iii) a chemometrics tool (the corner stone) to incorporate this information in a multivariate structured model and (iv) an image processing tool to visualise the data in all pixels of the image to build ‘chemical images’ of the tested meat quality traits”. Let us see how one can effectively employ this strategy in a particular scenario.

Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content

Four nondestructive technologies (i.e., acoustic firmness, bioyield firmness, visible and shortwave near infrared (Vis-SWNIR) spectroscopy, and spectral scattering) have been developed in recent years for assessing the firmness and/or soluble solids content (SSC) of apples. Each of these technologies has its merits and limitations in predicting the two quality parameters. With the concept of multi-sensor data fusion, different sensors would work synergistically and complementarily to improve the quality prediction of apples. In this research, the four sensing systems were evaluated and combined for nondestructive prediction of the firmness and SSC of ‘Jonagold’ (JG), ‘Golden Delicious’ (GD), and ‘Delicious’ (RD) apples. A total of 6535 apples harvested in 2009 and 2010 were used for analysis. Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by correlation coefficient and standard error of prediction (SEP). The SEPs for the firmness of JG, GD and RD using the best combination of two-sensor data were reduced by 13.5%, 20.0% and 7.3% for the 2009 data and 14.6%, 14.2% and 6.2% for the 2010 data; and using the best three or four fused sensor data by 19.1%, 24.9% and 13.9% in 2009, and 15.7%, 23.6%, and 8.9% in 2010, respectively. The combination of Vis-SWNIR and scattering data improved SSC predictions for RD apples, with the SEP values being reduced by 5.8% and 6.0% for 2009 and 2010, respectively. This research demonstrated that the fused systems provided more complete and complementary information and, thus, were more effective than individual sensors in prediction of apple quality.

Large partial band-gaps for Lamb waves in multiple phononic crystals thin plates

During the past decade, a growing attention has been paid to the Lamb waves propagating in composite plates due to a variety of applications for nondestructive evaluation, vibration attenuation and Lamb wave sensors. We present a revised plane wave expansion method and a finite element method to study the large partial band-gaps of a multiple Lamb wave phononic crystal thin plate with a symmetric mirror plane The results show that lots of partial stopbands of lower-order Lamb waves exist and can be substantially enlarged by using multiple heterostructures which consist of several pieces of phononic crystal with different ratios of the thickness (H) to the lattice period (L). These contribute to good mode choice for lower-order Lamb waves, which is believed to have much significance for optimially choosing models in Lamb wave nondestructive test and the one-way Lamb wave mode transmission.

Imposing Local Magnetic Fields to Control Magnetotactic Bacteria Through Combining Microfabrication and Magnetism

The ability to impose localized magnetic fields on magnetic bacteria has a variety of applications including in biophysics, nature inspired materials, and non-destructive sensors. Herein we demonstrate our ability to locally direct swimming magnetotactic bacteria, Magnetospirillum magneticum strain AMB-1, through microfabricated magnetic field concentrators. To accomplish this, a pair of Helmholtz coils was built to magnetize microfabricated permalloy structures of shapes including circles, squares, and triangles, which may affect the local magnetic fields near the structures. The permalloy structures were fabricated through photolithography and depositing a thin film of NiFe on a master mold. The substrate was a #1 glass coverslip that permitted us to observe the interactions of the magnetotactic bacteria with the permalloy structures. These microfabricated structures were positioned at the center and along the axes of the Helmholtz coils. The response of the bacteria around the structure was quantified through analyzing the images of the bacteria in terms of orientation and velocity. The results indicate that the AMB-1 appeared to cross or stop at the regions on the structures possessing higher magnetic field intensity relative to the surroundings. Furthermore, the bacteria appeared to alter their orientation as they approached sharp corners of the structures. We also developed a model using Finite Element Method Magnetics (FEMM) to investigate the regions of local magnetic field gradients, which indicated higher magnetic fields at the protrusions and the sharp points of the magnetic structures. This work supports the idea of using these magnetotactic bacteria as an aqueous non-destructive sensor and also has implications in fields including physics, biology, material science, and engineering.

Design of a low-cost non-destructive system for punctual measurements of salt levels in food products using impedance spectroscopy

The measurement of salt content in processed products still remains a problem to solve in the food industry due to the destructive nature of the conventional measurement methods. In this paper a method to assess salt content in food products such as cured ham or pork loin using impedance spectroscopy is presented. A low-cost system to carry out a punctual measurement of the impedance inside the food sample based on a non-destructive sensor was developed. The system includes an electronic equipment that allows the implementation of impedance spectroscopy and an electrode consisting of a coaxial needle. In order to validate the equipment, solutions with different salt concentrations were measured. The impedance modulus and phase values obtained for each frequency were processed using PLS; the results show a very good correlation between predicted and observed data. The same process was carried out on minced meat samples and again a good correlation was obtained. The PLS tool generated a model that permits prediction of salt levels in new meat samples.

A New Wireless Displacement Sensor Based on Reverse Design of Microwave and Millimeter-Wave Antenna Array

This paper introduces for the first time a new low-cost, nondestructive sensor to remotely monitor structural displacements (patent pending). The sensor consists of a two-element antenna array, operating at microwave or millimeter wave frequencies, remotely interrogated by a reader. The elements are stuck on the two sides of a crack to be monitored, no matter how large the crack is. The operating principle is based on the dependence of the array radiation characteristics on the element distance normalized to the wavelength. A combination of electromagnetic (EM) interferometry and Radio Frequency IDentification (RFID) technology allows the displacement of the antennas with respect to a reference configuration to be remotely determined. It is demonstrated that null measurements of the radiation pattern at microwave frequencies allow structural displacements to be predicted with uncertainties of the order of millimeters. The sensor accuracy is directly related to the wavelength of the radiated EM field.

Nondestructive Sensor Using Microwaves From Laser Plasma by Subnanosecond Laser Pulses

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Conventional ground-penetrating radar (GPR) requires large-aperture antennas or long-span measurements to survey a remote location precisely. We propose a laser-driven GPR (LGPR) as a new detection method. LGPR uses microwaves from laser-produced plasmas as remote transmitters and can survey a remote location using a compact instrument. We performed numerical simulations to investigate the radiation mechanism of microwaves from laser plasmas and confirmed the pulsewidth of the laser suitable for LGPR. Experiments with subnanosecond pulse lasers clarified the feasibility and detection performance of LGPR. </para>

Upward-looking ground-penetrating radar for monitoring snowpack stratigraphy

Operational remote monitoring of snowpack stratigraphy, melt water intrusions and their evolution with time for forecasting snowpack stability is not possible to date. Determination of the spatial variability of snowpack conditions on various scales requires a number of point measurements with various methods. These methods are either destructive or do not provide information about the internal structure of the snowpack. The application of a remotely controlled non-destructive sensor system would help to gain a higher spatio-temporal resolution about information of the snowpack. In this study we present results from upward-looking ground-penetrating radar (GPR) surveys from horizontal caves dug in the front wall of snow pits at the bottom of the snowpack. GPR data are compared with vertical profiles of snow hardness and density, obtained in the snow pit. Data were acquired in different areas with varying snow conditions with various GPR impulse systems, frequencies and polarizations. Radar experiments with high frequencies (> 1 GHz) detect internal layers in the snowpack in dry snow, but fail to provide clear reflections at the upper snow-air transition because of attenuation. In wet snow, the radar signals < 1 GHz are capable to penetrate a meter-thick snowpack and detect the snow surface, although the signal is strongly attenuated. Analysis of reflection phases and magnitudes allows interpretation of their physical origin in terms of changes in electrical permittivity. Varying antenna polarization causes a strongly different signal response, likely caused by the snow-pit wall present in our set-up. Forward calculation of density-based reflection coefficients between neighboring layers of varying hardness yields ambiguous results in terms of correspondence with observed radar reflections apart except for interferences of neighboring reflections. Moreover, we identify several pitfalls for future applications. The system set-up used here represents a basis for further developments towards a system, which is capable of improving information on the spatial and temporal snowpack characteristics.

A feasibility study on the use of near-infrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses

This work reports on the development and optimisation of NIRS technology for fat characterization both in live pigs and in carcasses in the slaughterhouse; use of this technology would enable implementation of a real-time traceability and control system based on non-destructive sensors. A total of 52 Iberian pig fat samples were analysed using a LabSpec ®Pro A108310 spectrophotometer (Analytical Spectral Device Inc.), with a high-intensity fiber-optic contact probe. Spectra were collected in five analysis modes: from the live animal, from the carcass in the slaughterhouse, from a subcutaneous fat sample with skin, from a skin-free subcutaneous fat sample and from a transverse section. Calibrations were developed for the prediction of the four main fatty acids in Iberian pig fat, obtaining for palmitic acid SECV values of 1.24% for in vivo analysis and 0.82% for carcass analysis, for stearic acid 0.67% and 0.94%, for oleic acid 1.42% and 1.48% and for linoleic 0.36% and 0.55%, respectively. The calibrations accounted for between 60% and 74% of the variation recorded in live animals, and between 31% and 87% of variation in carcasses. These results confirm the feasibility of NIRS technology for the on-site inspection and control of Iberian pig, both in the field and in the slaughterhouse.

&lt;b&gt;PENGUKURAN KANDUNGAN AIR TANAH PADA PERTANAMAN JARAK PAGAR (JATROPHA CURCAS L.)&lt;/b&gt;&lt;br&gt; SOIL WATER CONTENT MEASUREMENT UNDER JATROPHA CROP (JATROPHA CURCAS L.)

&lt;p&gt;Management strategies development for efficient water utilization of crop production requires sensitive measurements of changes in soil water content on a dynamic basis. Many of the methods currently used for measuring these changes are destructive, slow, or relatively expensive for large-scale investigations. A sensor that low-cost, nondestructive soil moisture sensor for measuring changes in soil volumetric water content on the basis of changes in the dielectric constant of the soil water were available. So, this research was carried out to quantify soil water content on Jatropha under rainfall condition, four levels of nitrogen fertilizer (N) and two population densities (P). The experiments used a systematic Nelder fan design with 9 spokes and 4 rings were conducted at SEAMEO-BIOTROP field experiment in 2007. Based on evaluation this instrument can use to measurement soil water content in various environment.&lt;/p&gt;

Actual Pathogen Detection: Sensors and Algorithms - a Review

Pathogens feed on fruits and vegetables causing great food losses or at least reduction of their shelf life. These pathogens can cause losses of the final product or in the farms were the products are grown, attacking leaves, stems and trees. This review analyses disease detection sensors and algorithms for both the farm and postharvest management of fruit and vegetable quality. Mango, avocado, apple, tomato, potato, citrus and grapes were selected as the fruits and vegetables for study due to their world-wide consumption. Disease warning systems for predicting pathogens and insects on farms during fruit and vegetable production are commonly used for all the crops and are available where meteorological stations are present. It can be seen that these disease risk systems are being slowly replaced by remote sensing monitoring in developed countries. Satellite images have reduced their temporal resolution, but are expensive and must become cheaper for their use world-wide. In the last 30 years, a lot of research has been carried out in non-destructive sensors for food quality. Actually, non-destructive technology has been applied for sorting high quality fruit which is desired by the consumer. The sensors require algorithms to work properly; the most used being discriminant analysis and training neural networks. New algorithms will be required due to the high quantity of data acquired and its processing, and for disease warning strategies for disease detection.

Non-Destructive Sensing of Atp Content as A Potential Indicator of Freshness of Spinach by Vis/Nir Spectroscopy

Non-destructive sensing of ATP content per protoplast of spinach (Spinacia oleracea) was examined through Vis/NIR spectra of intact spinach leaf. After obtaining the non-destructive Vis/NIR spectra, ATP content per protoplast was determined destructively during 3 days storage at 25°C. The ATP content was found to change as a quadratic polynomial function of storage time. In addition, the correlation between ATP content and Vis/NIR spectra during storage was excellent (R2 = 0.8245 for transmittance at 681 nm, and R2 = 0.9008 for the first derivative of transmittance at 760 nm). These results demonstrate the considerable promise in estimating the ATP content per protoplast with Vis/NIR spectroscopy which is a potential factor for evaluating the freshness of spinach.

Implementation of a Bayesian classifier using repeated measurements for discrimination of tomato fruit ripening stages

Quality control of postharvest fruits is moving towards substituting traditional sensory testing methods by more reliable quantitative methods. Ripening in fruits, such as tomatoes, is a complex phenomenon which affects chemical and physiological properties as a function of time. Attempts to solve the problem of ripening classification focus mostly on single sensors; however, there is not yet a complete solution. In this work, the use of repeated measurements obtained from two different non-destructive sensors incorporated into a Bayesian classifier for data fusion is proposed. Two independent measurements, from a novel non-destructive acoustic impact technique and from colorimeter sensors, were used. The problem of using repeated measurements to describe the probability density function for each class was addressed. The effect of correlated data and number of sensor characteristics on the artificial classifier was explained by an analysis of classification error. The results showed that the error classification rate is affected by the correlation between repeated measurements. The proposed Bayesian data fusion scheme reaches a classification error of as low as 5%, compared with 25–50% when single sensors are used.

Nondestructive Sensor Using Microwaves from a Laser Plasma

Ground penetrating radar (GPR) is a nondestructive sensor technology for detecting underground objects. GPR requires large-aperture antennas to survey a remote location precisely because of the expansion of microwaves. We propose a laser-driven GPR (LGPR) that uses microwave radiation from a laser plasma to achieve remote sensing. LGPR is expected to provide good spatial resolution with a small antenna. We selected a subnanosecond laser pulse as a suitable radiator for LGPR (L-S band). Experimental results show that the LGPR system can detect aluminum disks buried in sand.

Surface Flaws Detection Using AC Magnetic Field Sensing by a Thin Film Inductive Microsensor

Alternating magnetic field was used for detection of surface flaws on conductive metallic specimens. The nondestructive sensor probe was composed of the planar coil with inductive magnetic thin film yoke as a sensing component and a single straight typed exciting coil. The planar inductive coil sensor with magnetic yoke was fabricated by sputtering, electroplating, dry etching, and photolithography process. The exciting coil was generated by the alternative current with the range of 0.1-1.2 A with frequency rage of 0.6-1.8 MHz, respectively, which alternating current (ac) magnetic fields by exciting coil were applied to the specimen with artificial flaws. The specimens were prepared with the slit shaped artificial surface flaws (minimum depth and width; 0.5 mm) on metallic plate (Al; nonmagnetic metal and FeC; magnetic metal). The detected signal for the positions and shapes of surface flaws on specimens were obtained with high sensitivity and high signal to ratio by the planar inductive microsensor probe. The measured output signals by the noncontacted scanning on surface of coins (one cent and two euros) were converted to the images of the flaws. These results were compared with the optical images of real coins, respectively.

Damage detection in assembly fixtures using non-destructive electromechanical impedance sensors and multivariate statistics

Dimensional integrity of assembled products is a critical factor in defining product functionality and process productivity. It has been reported that fixture faults are one of the root causes of defective sheet metal assemblies. Therefore, integrating new sensor technologies into manufacturing process fixtures will have a direct impact on improving assembly system quality by helping to detect fixture faults faster. A piezoelectric impedance sensor combined with multivariate statistical process control (MSPC) is proposed to accurately monitor the structural integrity of locating fixtures. The new sensing and detection methodology reduces costs associated with production downtime due to faulty fixtures. MSPC is utilized due to its inherent advantages over current sensor monitoring techniques, mainly damage metrics. Previously used damage metrics fail to function properly if voltage or measurement variation is observed. Numerous false alarms result along with an inability to reference or calibrate a fixture to a healthy state. Introducing MSPC concepts and methodologies into piezoelectric fixture sensing systems decreases false alarms, enables healthy sensor state identification, and improves monitoring capabilities.

NIRS as a tool for precision horticulture in the citrus industry

Near-infrared spectroscopy was applied for non-destructively monitoring the soluble solids content of citrus fruit on trees. The model was built on refractometer readings and NIR spectra from outdoor measurements of fruit corrected for the ambient light conditions. The model was not significantly influenced by the variations in fruit temperature. The results showed that a high precipitation rate had a severe impact on the fruit quality. The average development of the cultivar-specific harvest date was used as a reference to calculate the relative harvest date of individual fruit indicating how non-destructive sensors could be integrated within the framework of precision horticulture.

Nondestructive testing for metallic flaws using inductive coil sensor with circular typed single loop excitation coil

AbstractAC magnetic field was used for the detection of flaws on surface of the conductive specimens. The nondestructive sensor probe was composed of the magnetic yoke coil as a sensing component and exciting single line coil. The excitation coil generated the AC magnetic fields with the frequency of around 27 kHz which were applied to the specimen with artificial flaws. The specimens were prepared with the various shape (slit and hole), size (minimum depth and width; 0.5 mm) of artificial flaws on each of material (Al, Cu; nonmagnetic metal, Fe, FeC; magnetic metal), respectively. The signal for the positions and shapes of surface flaws on metallic specimens were detected with high sensitivity with signal to noise ratio of over 15 by the magnetic yoke typed search coil. The images of flaws were obtained by measured output signal, which results were good coincidence with real flaws. (© 2008 WILEY‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)